Tag: Statistics

Bivariate Analysis for Quantitative Social Research

Bivaraiate analysis methods include contigency tables + chi square, Pearson’s R, and Spearman’s Rho,

Bivariate analysis involves analysing two variables at a time in order to uncover whether the two variables are related.

Exploring relationships between variables means searching for evidence that the variation in one variable coincides with variation in another variable.

There are a variety of techniques you can use to conduct bivariate analysis but their use depends on the nature of the two variables being analysed.

Type of variableNominal OrdinalInterval/ RatioDichotomous
NominalContingency table + chi-square + Cramer’s VContingency table + chi-square +Cramer’s VContingency table + chi-square +Cramer’s V, compare means and etaContingency table + chi-square +Cramer’s V
OrdinalContingency table + chi-square + Cramer’s VSpearmans’ rhoSpearmans’ rhoSpearmans’ rho
Interval/ ratioContingency table + chi-square +Cramer’s V, compare means and etaSpearmans’ rhoPearson’s RSpearmans’ rho
Dichotomous Contingency table + chi-square + Cramer’s VSpearmans’ rhoSpearmans’ rhophi
Bivariate analysis for different types of variable

Bivariate Analysis: Relationships, not causality

If there is a relationship between two variables, this does not necessarily mean one causes the other.

Even if there is a causal relationship, we need to take care to make sure the direction of causality is correct. Researchers must be careful not to let their assumptions influence the direction of causality.

For example, Sutton and Rafaeli (1998) conducted bivariate analysis on the relationship between the display of positive emotions by retail staff and levels of retail sales.

Common sense might tell you that positive staff sell more, however Sutton and Rafaeli found that the relationship was the other way around: higher levels of sales resulted in more positive emotions among staff. This was unexpected, but also makes sense.

Sometimes you can infer the direction of causality with 100% certainty. For example with the relationship between age and voting patterns. Younger people are less likely to vote, and thus age must be the independent variable. There is no way voting patterns can influence age.

Contingency Tables

A contingency table is like a frequency table but it allows two variables to be analysed simultaneously so that relationships between them can be examined.

They usually contain percentages since these make the relationships easier to see.

MaleFemale
NumberPercentNumberPercent
Sociology603012040
Maths20106020
English20106020
Dance100506020
200100300100
Students studying subjects in one college, by gender.

The table above contains both the numbers of the variables and their percentages as a proportion of the total next to them.

The percentages are column percentages: they calculate the number in each cell as a percentage of the total number in that column. Hence why the percent columns add up to 100!

In the above table we can see that there are more female students than male students and females dominate in every subject other than dance, because dance is much more popular among male students. (It’s quite an unusual college!)

Contingency tables can be applied to all types of variable, but they are not always an efficient method.

Pearson’s R

Pearon’s R is a method for examining relationships between interval/ ratio variables. The main features of this method of analysis are:

  • The coefficient will lie between 0 and 1 which indicates the strength of a relationship. 0 means no relationship, 1 means a perfect relationship.
  • The closer the coefficient is to one, the stronger the relationship, the closer to 0, the weaker the relationship.
  • The coefficient will either be positive or negative which indicates the direction of the relationship.

Examples of Pearsons’ R correlations

The table below show the relationship between age and four other variables. (Note this data is hypothetical or made up and for illustrative purposes only!)

Age grouphappiness scorewealth £hours watching TV per weekave no of friends
2010£10,000155
308£20,000108
406£30,0003311
504£40,0002210
60-692£50,000916
Pearson’s R-1100.93

The correlations are as follows:

  • between age and happiness: perfect negative correlation.
  • between age and wealth: perfect positive correlation.
  • between age and watching TV: no correlation
  • between age and number of friends: strong positive correlation.

The scatter plots for the above data are as follows:

Age and happiness

Age and wealth

Age and TV

Age and friends

Spearman’s Rho

Spearmans’ Rho is often represented with Greek letter p and is designed for use with ordinal variables. It can also be used when one variable is ordinal and the other is interval/ ratio.

It is exactly the same as Pearson’s R in that the computed value will be between 0 and 1 and either positive or negative.

Pearson’s R can only be used when both variables are interval/ ratio. Spearman’s Rho can be used when on the the variables is ordinal.

Phi and Cramer’s V

The Phi coefficient is used for the analysis of the relationship between two dichotomous variables. Like Pearsons R it results in computed statistic which is either positive or negative and varies between 0 and 1.

Cramer’s V can be used with nominal variables. It can only show the strength of relation between two variables, not the direction.

Cramers’ V is usually reported along with a contingency table and chi-square test.

Comparing means and eta

If you need to examine the relationship between an interval/ ratio variable and a nominal variable if the latter can be relatively unambiguously identified as the independent variable, then it might be useful to compare the means of the interval/ratio variable for each subgroup of the nominal variable.

This procedure is often accompanied by a test of association between variables called eta. The statistic expresses the level of association between the two variables will always be positive.

Eta-squared expresses the amount of variation in the interval/ ratio variable that is due to the nominal variable.

Signposting and sources

This material should be of interest to anyone studying quantitative social research methods.

To return to the homepage – revisesociology.com

Bryman, A (2016) Social Research Methods

Univariate Analysis in Quantitative Social Research

Univariate analysis reviews one variable at a time and typically uses frequency tables and diagrams like bar charts and pie charts. Measures of central tendency and dispersion are tools for analyzing data, with central tendency often involving the mean, median, or mode while dispersion relies on range and standard deviation. Understanding these statistical methods aids in the comprehension of data distribution in areas of interest such as wealth statistics.

Univariate analysis refers to the analysis of one variable at a time.

The most common approaches are:

  • Frequency tables.
  • Diagrams: bar charts, histograms, pie charts.
  • Measures of central tendency: mean, median, mode.
  • Measures of dispersion: range and standard deviation.

Frequency Tables

A frequency table provides the number of cases and the percentages belonging to each of the categories for a variable. Frequency tables can be used for all the different types of variable.

Below is a simple example of a frequency table showing the number of schools in three different categories of the ‘type of school’ variable for the 2022-2023 academic year. I rounded the percentages below.

Type of schoolNumber of schoolsPercent
Local Authority1185848
Academy 1017642
Independent 240810
Total24442100
Number of schools by type of school, England and Wales 2022-23.

Analysts usually clean from raw data to make frequency tables so people can understand and visualise them more easily.

Frequency tables are the starting point for generating diagrams which put the data into visual form making trends stand out.

Diagrams

Diagrams representing quantitative data in visual form to make data easier to understand and interpret. Bar charts and pie charts are two of the most commonly used visual representations of quantitative data.

Bar charts

The chart below shows the same data as in the frequency table above. Each bar represents one of the three school types.

The bar chart below shows the largest category is Local Authority (LA) maintained schools with academies the second largest category. You can also see there are relatively few independent schools.

Bar chart showing different school types in England and Wales.

Pie Charts

The main advantaged of a pie chart is that you can see the proportion of each category in relation to the total. A pie chart shows this sense of relation to the whole more clearly than a bar chart.

For example you can clearly see below that LA Maintained schools make up nearly 50% of the total. This doesn’t stand out as much in the bar chart.

You can also see that Independent schools represent around 10% of schools from the pie chart.

Pie chart showing number of LA maintained schools, academies and independent schools in England and Wales.

Frequency tables and diagrams: final thoughts

Diagrams are useful to make frequency tables easer to understand.

Bar charts are more useful when you want to look at proportions in relation to each other. Pie charts are more useful when you want to look at proportions in relation to the whole.

Keep in mind however that charts are only as useful as the data. For example, one limitation with the above data is that it tells you nothing about pupil numbers, only school numbers!

Sources

Gov.UK (accessed July 2023) Schools, Pupils and their Characteristics 2022-23.

Measures of Central Tendency

Measures of central tendency encapsulate in one figure a value which is typical for a distribution of values. In effect, we are seeking out an average for a distribution.

Quantitative social research analysts recognise three different forms of average:

  • mean
  • median
  • mode
the difference between mean, median and mode shown in a bar chart.
Diagram 1: Mean, median and mode for a random distribution of ages.

Arithmetic mean

The mean is the sum of all values in a distribution divided by the number of values.

In diagram one above, we add ALL the ages together and divide by 20 which is the total number of ages in the sample. This gives us a mean of 51.6.

The mean should be applied to interval/ ratio variables. It can also be applied to ordinal variables too.

Median

The median is the mid-point in a distribution of values. We arrive at the median by lining up all the values smallest to largest and then finding the middle value.

Whereas the mean is vulnerable to outliers which are extreme values at either end of the distribution. Outliers can greatly increase or decrease the mean, but they have much less of an affect on the median.

We see this in diagram one above, where the median point is 45.5, considerably lower than the mean of 51.6. In the case above the mean is higher because the oldest four people skew the mean average upwards. The four oldest are a lot older than the people in the middle, compared to the average ages of the rest of population.

The median can be used in relation to both interval/ratio and ordinal variables.

Mode

The mode is simply the value that occurs most frequently in a distribution. The mode can be applied to all types of variable.

In the diagram above, the mode is 28, because that is the only age which occurs twice.

Median more useful than the Mean?

With social data it is often more useful to know the median rather than the mean. This is especially true with wealth statistics in the UK.

Wealth and income distribution are of special interest to sociologists, because there is a lot of variation in distribution. Neither wealth nor income are equally distributed. Understanding how they are distributed has significant implications for life chances and social policy.

raw data showing UK wealth distribution
Table showing household wealth distribution in the UK by decile, 2018 to 2020.

Visualising the total wealth in a bar chart looks like this:

Bar chart showing UK wealth distribution 2018-2020.

Here you can clearly see a skew towards the top two deciles, especially the first decile. The richest 10% of households have an average of almost £2 million in wealth, which 8 times more than even the 4th decile.

In cases where there is a lot of variation in data, in terms of a large skew showing up at one end, as above, then get the mean and median being very different.

in the chart above the mean is £489 000, pulled up by the huge relative wealth of the top 20%.

The median wealth is only £280 000 and 50% of people have less than this.

Mean wealth in the UK gives you a misleading picture of the amount of wealth most people in the UK have!

Sources

ONS: Household Wealth in the UK, 2018-2022.

Measures of Dispersion

Measures of dispersion show the variation in a distribution.

Two measures of dispersion include:

  • the range (the simplest)
  • the standard variation.

Range

The range of data is the distance between minimum and maximum values in a distribution. Like the mean, outliers can greatly affect the range.

The range of household wealth (grouped by decile) in the UK is £1.9 Million (see chart below).

This is a very simple measure which doesn’t tell us vary much about how much wealth ordinary people.

For example it doesn’t tell us that the top decile of households are almost twice as wealthy as the next decile down.

Standard Deviation

We calculate the standard deviation by taking the difference in each value in a distribution from the mean and then dividing the total of the differences by the number of values.

The standard deviation is the average amount of deviation around the mean.

For example, the standard deviation of wealth in the UK (grouped by decile) is £575 211.

Outliers don’t affect the standard deviation as much as the range. The impact of outliers on the standard deviation is offset by dividing by the number of values.

Box Plots

Box plots are popular for showing dispersion for interval/ratio variables.

The box plot provides an indication of both the central tendency (median) and dispersion (outliers).

The box plot of wealth below treats the top richest decile as an outlier. It clearly shows you the skew is the top.

The box shows you where the middle 50% of households sit: between £800 000 and £50 000.

The line in the box shows you the median value of household wealth: £280 000.

Box plot of UK wealth.
Box plot of wealth, UK 2018-2020

The shape of a box plot will vary depending on whether cases tend to be high or low in relation to the median. They show us whether there is more or less variation above or below the median.

Sources

ONS: Household Wealth in the UK, 2018-2022.

Boxplot generator.

Signposting and related posts

This material is most relevant to the Research Methods module. It might be a little advanced for A-level sociology. You are more likely to need this during a first year university statistical methods course.

To return to the homepage – revisesociology.com

Home working reinforces traditional domestic roles…

but flexible work hours leads to more gender equality at home.

An analysis of six years of longitudinal data from between 2010 and 2016 has found that home working reinforces a traditional gendered division of domestic labour while flexible working leads to a more equal domestic arrangement. 

The research analysed data from the UK Household Longitudinal Study (2010-2016) which surveyed 1700 working parents with at least one child aged under 12.

Overall, women spend more than twice as long as men doing housework. Women reported doing 13.4 hours of housework a week on average, men reported doing an average of 5.5; while 54% of women reported being primarily responsible for childcare.  

Further data analysis adjusted the stats for income, education level, ethnicity, age and neighbourhood to isolate the effect of working from home on childcare and housework.

Fathers working from home were half as likely to report they were sharing child care compared to those who were not working from home, with men fearing they may lose their masculinity when taking on more routine tasks.

Whereas women working from home were twice as likely to report they were primarily responsible for childcare compared to those who were not working from home. 

The effect was greater for lower income couples: women doing low income jobs at home spent proportionately more time doing domestic work than women in higher income jobs. 

graphs showing how gender equality at home changes with working from home and flexible working hours.

Flexible working hours led to a more equal gendered division of labour

Flexitime, where men and women have some degree of control over their working hours (days of the week/ start and finish times) led to a more equal division of domestic labour. 

Conclusions and relevance

The broad conclusions are that working from home does not benefit women, but flexible working arrangements do, so if we want to see a more equal division of labour and childcare we want to push for more flexible working hours, not necessarily more home-working hours! 

You need to be careful when using this research as the results are open to interpretation.

If we just allow men and women to work at home then this reinforces traditional gendered divisions of labour. This suggests that if the domestic sphere is further isolated from society this results in ‘patriarchal norms’ being reinforced. This seems to suggest support for the radical feminist view that the isolated, privatised nuclear family is oppressive to women: as they end up doing more domestic labour, men end up doing less when both partners do more paid work from home.

HOWEVER, the fact that more flexible working hours results in more gender equality in how domestic chores are divided offers support for liberal feminism: when men and women are both working but more flexibly, this breaks down the oppressive traditional division of labour, but this requires men and women to be out at work.

Overall, it suggests that a good social policy change would be to introduce more flexible working hours in general, but that pushing for more home based working isn’t such a good idea, if we are interested in more gender equality at home that is!

Limitations of this research

One limitation of this survey is the relatively low sample sizes for those home working and doing flexitime. 

Only 7% of men used working from home arrangements, and only 5% of women. Only 15% of women used flexitime, and only 11% men. 

This means with a sample size of 1700, only around 50 men would have been working from home in that sample, and once you control for income, location, and ethnicity you have some very small sub-samples, for example. 

Sources and Signposting

Heejung Chung and Cara Booker (August, 2022) Work, Employment and Society: Flexible Working and the Division of Housework and Childcare: Examining Divisions across Arrangement and Occupational Lines.

This material is mainly relevant to the families and households module, usually taught as part of the first year within A-level sociology.

How Motherhood and Fatherhood affect paid and domestic work

mothers are more likely to take time off work and do 10 hours more housework and childcare than fathers.

One of way of measuring the relative effects of motherhood and fatherhood on paid and domestic labour is to compare the following two subsets:

  • Mothers in relation to women without dependent children compared to
  • Fathers in relation to men without dependent children.

Comparing these two subsets would be a useful contribution to evaluating Liberal and Radical Feminist theories about how family life affects women. Broadly speaking:

  • Liberal Feminists claim that family life (compared to women remaining childless) has little or no negative impact on women.
  • Radical Feminists claim that family life has a negative impact on women, as women are more likely to quit their jobs when children are born, and they end up doing more childcare than men, and continue to do more housework too, suffering from the triple shift.

Generally speaking if mothers are doing less paid work and more domestic work than women without dependent children, while fathers are doing more paid work and less domestic work than men without dependent children, it’s reasonable to say this suggests more support for radical compared to liberal feminism.

HOWEVER, we’d still need to do further research to test this out: statistics don’t give us in-depth data and allow us to conclusively prove or dismiss either of these broad theoretical positions, they just point in one direction or the other.

This post looks at the following data taken from the ONS’ (1)

  • The percentages of mothers, fathers and men and women without dependent children in employment
  • The percentage of mothers in full time work by age of child
  • The percentages of 24-35 year old mothers and fathers in work.
  • How much housework mothers and fathers do.

You can view all of the stats below on my Tableau page.

Motherhood and fatherhood encourage traditional gender roles

The graphic below shows the percentages of mothers, fathers and men/ women without dependent children in paid employment 2002-21, U.K.

In 2021 72% of men without dependent children were in work compared to 92% of fathers. 69% of women were in work compared to 76% of mothers.

So… both men and women with children are more likely to be in work compared to those without children (but this data also includes retired people, so no surprise, maybe!)

What’s interesting is the relative difference between men and women without children and mothers and fathers:

Mothers are much less likely to be work than fathers, the figures for men and women without children in work are much closer together.

This suggests having children is more likely to result in women leaving paid employment to take on a caring role while having children encourages men into the breadwinner role.

Only 30% of women with new born children work full-time

Bar chart showing percentage of mothers in full time work by age of child.

It’s probably unsurprising, but only 30% of women with very young children aged one, and the percentage increases gradually until 49% of women with 18 year olds are in paid employment.

This is a clear trend of women taken a period of employment and then gradually returning in greater numbers as their children get older.

The figures for men hardly change at all with children being born (not shown on graphic).

Young women are affected most

This statistic is the strongest evidence of how motherhood has a detrimental affect on women’s careers compared to fatherhood.

bar chart comparing number of young mothers and fathers in work, UK 2022.

For 24-35 year olds, MORE women without dependent children are in paid work than men.

However, only 69% of 24-35 year old mothers are in employment compared to a massive 92% of fathers in the same age category.

Women do more housework and childcare

In 2022 women did 30 minutes more unpaid housework per day than men and they did one hour extra of childcare.

Over the course of a week, this means women with dependent children are doing 10 hours more childcare and housework combined than men.

This seems to be strong evidence of mothers suffering from the triple shift.

Conclusions: support for radical feminism?

The above statistical evidence seems to offer some support for the radical feminist view that families are harmful to women, in that having children results in women being more likely to take time off paid-work compared to men and mothers doing 10 hours more domestic labour and childcare per week than men.

Sources and Signposting

This material is most relevant to the families and households module, usually taught as part of the first year A-level sociology course.

To return to the homepage – revisesociology.com

(1) Office for National Statistics: Families and the Labour Market UK, 2021.

Screenshots of Tableau embeds:

women and men in paid work
bar chart showing hours per day childcare and domestic labour done by mothers and fathers, UK 2022.

Why don’t young people like the Monarchy?

Young people are twice as likely to NOT support the monarch as old people, but why is this?

Attitudes towards the British monarchy vary significantly by age.

According to a recent YouGov survey commissioned by the BBC’s Panorama (1)only 32% of 18-24 year olds think we should continue to have a monarchy compared to 67% of 50-64 year olds.

So more than twice the proportion of 18-24 year olds are against the idea of continuing the monarchy compared to 15-64 year olds.

When you stretch the age gap further you find the difference is even greater: Only 26% of 18-24 year olds think the monarchy is good for Britain, compared to 72% of over 65 olds:

The difference is certainly significant, but why is there such a remarkable difference in attitudes towards the monarchy between younger and older Britons?

Lifecycle or Cohort Effect?

Is this stark difference in attitudes towards the monarchy down to a lifecycle or cohort effect?

  • A lifestyle effect would mean that all younger people in general, from any generation, start off viewing the monarch less favourably and as they get older view the monarchy more favourably.
  • A cohort effect would mean that there is a difference in attitude across the younger and older generations, in which case we can expect younger people to keep their more negative attitudes towards the monarchy as they get older.

Of course it is also possible that BOTH of the above effects are at work: intuitively it makes sense that the monarchy is becoming less relevant over time AND that as people get older they are more likely to defer to authority.

One way determining the relative strength of each effect would be to ASK older people whether they used to support the monarchy or not (although there are potential validity flaws related to memory in this), so a more valid measure would be to look at PAST opinion polls on the monarchy.

If we go back to this 2020 survey on whether Britain should have a monarchy the results for the older age groups are slightly higher to that of the 2023 survey suggesting what we are seeing here is a cohort effect, rather than a lifecycle effect.

What is surprising is that 52% of 18-24 year olds reported wanting a monarchy only three years ago…

To my mind this possibly suggests what is known as a ‘period effect’ – where a significant event effects public attitudes, and this case the event was the death of the Queen and forthcoming coronation of the new King: people simply aren’t that keen on King Charles compared to the Queen, and maybe this has had more of an impact on younger people.

Also there is the negative press associated with Prince Andrew and his love of sleeping with teenage girls, which probably didn’t do the institution many favours in the eyes of 18-24 year olds!

If you go back further, you can find a lot of historical polls on public attitudes towards the monarchy, but it’s hard to find anything which is split clearly by age cohort, so we are left with national average figures.

In general there is broad support here for there being a cohort effect – if we go back 20 years we see nearly 80% supporting the monarchy, compared to just over 50% on average today, and given that we’ve got an ageing population clearly people aren’t changing their minds and becoming more pro-monarchy as they get older!

In some of these polls people are even asked ‘what should happen to the monarchy after the Queen dies’ and it’s clear that there was less support for the monarchy in that previous hypothetical situation compared to when the Queen was alive, and we see that being played out in the statistics now!

Why do young people show less support for the monarchy?

I don’t know of any research looking at WHY the younger generation are less likely to support the monarchy compared to the older generation, but decades of surveys give us some kind of idea and we can theorise about why more broadly, based on social changes over the past decade.

First of all it seems there has been an immediate decline in support for the monarchy based on the death of the Queen. We see this in the relatively rapid decline in pro-monarchy attitudes in 2023 compared to 2020.

This makes intuitive sense: even teenagers today would have ‘grown up’ with the Queen, and more so for older people. Media coverage of the Queen was always very positive and she’s been a mainstay of British popular culture for decades, whereas our new King Charles has received much more negative press (‘the crazy organic guy’) and Camilla isn’t that popular, and he simply doesn’t have the historical kudos of the Queen: he doesn’t link us back to this warm and toasty (albeit mythical) 1950s feeling like the Queen did.

And then think of the turmoil the Royal Family has gone through over the last decade: with Meghan and Harry leaving and Andrew’s taste for teenage girls, it’s all a bit sordid, they’re just a bit all over the place, they simply don’t represent wholesomeness in the same sort of way the Queen did.

So it kind of makes sense that once the Queen is dead, there’s not a lot positive within the monarchy to support anymore.

Younger generations may also be less inclined to support the monarchy because they haven’t grown up with just television: personalised feeds mean younger people are probably much less exposed to BBC representations of royalty, less likely to get news items about royalty and when they do they will be presented more as media celebrities rather than anything special.

Possibly national identity means less to younger people in a global age, and royalty are ‘British’, so maybe they are less supported because of this.

I’d like to think that there’s a sense of injustice about so much tax payers’ money being spent on this defunct institution when they are already so wealthy, but I doubt this is much of a thing, maybe for a few percent it is though.

Signposting and Sources

These statistics seem to be evidence of the broad shift towards postmodern society. The fact that young people have such different attitudes towards the monarchy than older people suggests a degree of social fragmentation, certainly not anything like value consensus.

Declining support for the monarchy also suggests we are less likely to defer to authority and hierarchy based on tradition, and presumably more likely to decide for ourselves what we should be doing with our lives.

BBC News (April 2023) How Popular is the Monarchy Under King Charles?

To return to the homepage – revisesociology.com

A-Level Sociology Growing in Popularity!

A-level sociology entries increased by 23% between 2018 and 2022.

The number of students studying A-level sociology has increased significantly over the past few years. In summer 2022 there were 43 590 A-level sociology exam entries, compared to only 33, 420 in summer 2018. This represents a 23% increase over four years.

Sociology is now the fifth most popular A-level sociology subject, more popular than history!

Why is sociology growing in popularity?

This isn’t just because there are more people studying A-levels in general. Some other subjects have also been growing in popularity, most notably psychology, but OFQUAL notes that most other high demand subjects have seen stable numbers over the past four years.

So my working theory is that young people are increasingly looking at contemporary society, seeing the many and increasingly urgent amount social problems facing us and they want answers, and these are maybe answers that the regular school curriculum cannot provide.

Over the past four years while in secondary school students have lived through several unforeseen and tumultuous events such as Brexit, the mega-corruption within the Tory part, the covid-19 pandemic and all of this in the context of global warming and climate change and the continued failure of governments around the world to do anything significant about this global crisis.

Also, increasing amounts of teenagers would have lived through declining living standards as their parents’ real term wages have been eaten into because of inflation, which has a much longer history than just the previous year when inflation went into overdrive.

All of this means young people are probably increasingly looking at the world and their future prospects and are worried, and want answers, and sociology really can help with his.

Of course it is also understandable that A-level psychology numbers are increasing more rapidly. Young people today have been socialised into an individualistic world view and they probably think psychology can help them understand their heightened sense of anxiety, which is a totally understandable response to our crisis ridden world and the inaction of practically every adult in power.

The problem is psychology can only go so far in its ability to explain social problems and the mental health ‘pandemic’ among young people. They need good old sociology to understand the material conditions which are the root cause of their declining prospects!

Sources

OFQUAL Official Statistics (26 May 2022) Provisional entries for GCSE, AS and A level: summer 2022 exam series.

Ethnic Inequalities in the U.K.

Most ethnic groups in Britain are poorer than the white majority.

There are several dimensions to inequality by ethnicity in the United Kingdom.

Pakistani and Bangladeshi households have among the lowest levels of wealth and income compared to White Households while Black African households have high wealth but lower income.

Despite their wealth and income, however, White British people have the second lowest life expectancy of all ethnic groups, and despite their relative poverty Bangladeshi women have the second highest life expectancy!

Household Wealth Inequalities by Ethnicity

There are significant differences in household wealth by ethnicity.

The median household wealth in the U.K. between April 2016 and April 2018 (the latest data available) was £286 600. The range between the most and least wealthy ethnic groups was £314 000 for the White British Group and £34 000 for the Black African Group.

This means that White British households are nine times wealthier than Black-British African households.

All ethnic minority groups have less household wealth compared to White households, except for Black Caribbean households who are wealthier:

If we rank ethnic minority groups in order of household wealth from richest to poorest we get the following:

  1. Black Caribbean
  2. White
  3. Chinese
  4. Indian
  5. Bangladeshi
  6. Black African
  7. Pakistani.

However these differences in household wealth are partly a reflection of two other factors which have a major influence on wealth

  1. The different age profiles of ethnic minority populations. Black Africans especially have a lower age profile than average, in other words they are younger overall, and age has a huge influence on household wealth. Those in the 55-64 age group are 10 times wealthier than those in the 25-34 age group.
  2. Home ownership. Houses tend to be the largest capital investment a family or individual has, and the value of houses is included in the measurement of wealth used by the ONS. Home ownership rates vary by ethnicity and Black Africans have relatively low rates of home ownership, which is in turn partly a reflection of the lower age profile.

Income Inequality by Ethnicity

Income inequality varies by ethnicity. The statistics below (2) are for the period 2017 to 2020 (the latest available at time of writing in January 2023).

Focusing on which ethnic groups are most likely to be in the bottom two quintiles for income, ranking the poorest first:

  • 76% of Pakistani and 75% of Bangladeshi households are in the bottom two quintiles.
  • 75% of Bangladeshi households
  • 62% of black households are in the bottom two quintiles
  • 49% of Chinese households are in the bottom two quintiles
  • 40% of Asian and 38% of households are in the bottom two quintiles.

So to summarise Pakistani and Bangladeshi households are almost twice as likely to be in poor households compared to the average (which is 40% which is the poorest two quintiles or 2/5 or 4/10!).

Black households are 1.5 times more likely and Chinese households are slightly more likely to be poor.

Indian and White British households are at 40%, which is in line with the national average.

The figures are similar for the chances of having a high income household by ethnicity.

Ethnic Differences in Life Expectancy

Life expectancy at birth for females varies by ethnicity from 83.1 years for those of mixed ethnicity to 88.9 years for those of Black African ethnicity.

The differences here don’t seem to correlate at all with inequalities in wealth and income.

For example the White ethnic group has the second lowest life expectancy despite having some of the highest wealth and income.

Also Bangladeshi and Pakistani females have differing life expediencies despite having very similar levels of household income.

The pattern is similar for males but with overall lower life expectancy in all ethnic groups.

Signposting

This material is of general relevance to a whole range of sociology modules, from education to crime and deviance!

You might also like this post – another aspect of ethnic inequalities covered elsewhere: educational achievement by ethnicity.

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Sources

(1) Office for National Statistics (November 2020) Household wealth by ethnicity, Great Britain: April 2016 to March 2018.

(2) GOV.UK Income Distribution by Ethnicity.

(3) Office for National Statistics: Ethnic Differences in Life Expectancy.

The increasing cost of Christmas

25% of people say they can’t afford the Christmas they want in 2022, double the number from 2021.

The cost of Christmas is up by around 20% in 2022, and almost 40% say the cost of Christmas makes the event too stressful, but despite these woes, 70% say that ‘cancelling Christmas is not an option’.

These are some findings from a recent YouGov survey and in this post I consider how all of this might be relevant to sociology!

How much does the average person spend on Christmas…?

The average person in Britain plans to spend £642 on Christmas in 2022, which is down only slightly on 2021 when the average person spent £670. (These are Mean, not median averages).

However given inflation, people will be getting a lot less for their money this year even though the reduction in raw expenditure isn’t that significant!. According to The Guardian the cost of our various Christmas expenditures – mainly presents, food and, for some, travel have risen by more than 20% this year compared to 2021….

This basically means everyone’s going to be having one less slice of turkey, maybe a couple of less potatoes, and, worst of all, fewer pigs in blankets (yes, things really are THAT bad!)

25% of people can’t afford the Christmas they want

Given that the cost of Christmas has risen sharply it’s not surprising that the number of people saying they cannot afford the Christmas they want has doubled to 25%.

This proportion sounds about right based on the poverty stats: about 20% of the UK population are in relative poverty and I imagine most of the people responding positively to that question are going to come from this 20%.

Of course not all of them will, several people on low incomes budget for Christmas by saving all year round, and some of those responses will be more middle-income families having to cut down on their usual more affluent Christmas.

I do find it interesting that 75% are happy enough with their finances to be able to afford the Christmas they want, suggesting that people aren’t that sucked into the consumerist hype – the average figure of £650 seems to be adequate.

Maybe that’s a fail for the Christmas hype-machine, further suggesting that people aren’t as passive as you might think?!?

40% say Christmas is too Stressful

This is depressing – a significant minority of the population find the event too stressful because of the money…

This means that maybe that the veneer of Christmas is something of a lie, while underneath at the micro-level there’s a lot of suffering going on.

Value Consensus around Christmas?

Besides the increasing cost of Christmas and the increasing numbers of people feeling stressed about it and going into debt to fund it, nearly 70% of Britons say that ‘cancelling Christmas is not an option’

And it’s very rare these days that you get that many people to agree on anything, and so celebrating Christmas is maybe one of the few points of value consensus that we have.

Or is this value consensus at the level of society? Christmas is one of the few periods of the year where we all get to retreat from the world of work and society and spend some time with our families, so maybe here Britain is saying ‘we value being able to retreat to our private households’, so one could interpret this as being anti-social.

Signposting

This is really just a bit of annual Christmas fun with statistics!

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Discrimination against LGBTQ people in the UK

This post summarises some of the most recent data on the extent of discrimination against LGBTQ people, and is aimed at A-level sociology students studying aspects of sex and gender and gender inequality across the A-level specification.

The LGBTQ survey carried out in 2018 by the Government Equalities Office found that:

  • LGBT respondents were less satisfied with their life than the general UK population (rating satisfaction 6.5 on average out of 10 compared with 7.7).
  • Trans respondents had particularly low life-satisfaction scores (around 5.4 out of 10)
  • 40% of respondents had experienced verbal harassment or physical violence because they were LGBTQ in the last 12 months.
  • 2% had undergone conversion therapy.

The above survey was based on a sample of 108 100 respondents and was hosted online for a total of 12 weeks.

There was also some evidence from this survey that there is discrimination against Trans people when applying for work, but this is only based on one response…

The 2018 Trans Report from Stonewall found that:

  • A third of trans people have been discriminated against because of their gender identity when visiting a café, restaurant, bar or nightclub in the last year.
  • More than a quarter of trans people in a relationship in the last year have faced domestic abuse from a partner.
  • More than 44 per cent avoid certain streets because they don’t feel safe there as an LGBT person.

The 2018 StoneWall Work Report found that 20% of LGBTQ people had faced some sort of negative discrimination because of their sexual identities in the workplace…

Government Data for England and Wales shows that Hate Crimes against people based on sexuality has been increasing every year since 2015. The latest data show that:

  • 54% of Transgender people reported experiencing a negative incident outside of the home because of their sexuality compared to 40% of gay people.
  • 11% of Transgender and 5% of gay people reported being victims of physical violence.
  • NB around 90% of these incidents were not reported to the police! These are from victim survey results!

(Link to more detailed report on sexuality hate crime).

It’s from the USA but still interesting as a point of comparison…. Trevor’s National Survey on LGBTQ mental health, based on a sample of 35 000 LGBTQ 13-24 year olds found that…

  • 75% had experienced discrimination based on their gender or sexuality at least once in their lifetime.
  • 42% had seriously considered suicide in the last year, with more than 50% of transgender and non binary youth reporting this.
  • 13% reported being subject to conversion therapy .

Relevance to A-level Sociology

Sex and Gender inequalities are one of the core aspects taught across A-level sociology, but statistics and research on sexuality and transgender issues are lacking in most of the A-level text books.

This post is an attempt to make this increasingly relevant aspect of gender and gender identity more accessible.

From a research methods point of view it’s worth noting how little research and monitoring are done on LGBTQ inclusion and discrimination – for example the latest nation wide government survey above was four years ago in 2018.

The Covid-19 Pandemic Exaggerated Health Inequalities in England

The Pandemic has increased health inequalities in England, according to a recent report by the Institute of Health Inequalities – Build Back Fairer – The Covid-19 Marmot Review: The Pandemic, Social and Health Inequalities in England.

Prior to the Pandemic, from 2010 to 2020, health inequalities between the least and most deprived were increasing in England.

Pre-pandemic, increases in life expectancy had stalled, but life expectancy for the most deprived 10% of the population actually decreasing in some regions (such as parts of the North East and London) during some years in that 10 year period.

Covid-19 increased health inequalities

The charts below show the mortality rates per one thousand between March and July 2020.

As you can see, there are drastic differences already between the least and most deprived deciles – 600/ 100 000 for the poorest decile, compared to 400/ 100 000 for the wealthiest decile.

But the difference is greater when we look at the covid related mortality rate – this is 200/100 000 for the poorest, compared to nearly 100/ 100 000 for the wealthiest.

So health inequalities increased from a difference of 1.5.1 to nearly 2:1 as a result of the Pandemic.

Some of this difference is explained by the different levels of exposure due to occupation – as a general rule, professional workers are more able to work from home and stay isolated, while manual workers and care workers need to actually go to work in person, and this is reflected in the different mortality rates by occupation (‘social class’) for the same period as above:

Explaining health inequalities… it’s not ALL about the Pandemic

Professor Marmot is at pains to point out that these health inequalities were in existence BEFORE the pandemic, and that government health policies between 2010 to 2020 explain WHY poor people have died in such huge numbers from covid-19 and why England has the highest covid related mortality figures in Western Europe.

In particular Marmot points to the following government policies:

  1. A political culture that undermined social inclusivity and cohesiveness and failed to promote the common good
  2. Widespread inequality, which is bad for socio-economic outcomes in general, with the most deprived ‘steered’ towards poor living conditions and unhealthy lifestyles.
  3. Government austerity policies – an underfunded health and social care sector.

In terms of what to do, the report makes a number of suggestions, mainly to do with introducing policies to improve health outcomes of the most deprived, and this will take a broader/ deeper approach to social change rather than just being about health!

Relevance to A-level Sociology

This is a VERY sociological report – putting the covid mortality rate in longer term context.

The point is that we can’t just blame the Pandemic for killing people – certain types of people (the poor) died in larger numbers proportionality to the rich – which means there was a social cause to the high covid death toll in England.

And that cause was, according to this report, already high levels of existing inequality.

This is a rare example of some long-term quantitative analysis, it sounds almost like Functionalism/ Positivism in its approach.

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